- Introductions
- Class overview
- Getting R up and running
Photo by Belinda Fewings on Unsplash
Welcome to class!
About Us
Carrie Wright
Assistant Scientist, Department of Biostatistics, JHSPH
PhD in Biomedical Sciences
Email: cwrigh60@jhu.edu
Website: carriewright11.github.io
About Us
Ava Hoffman
Research Associate, Department of Biostatistics, JHSPH
PhD in Ecology
Email: ava.hoffman@jhu.edu
Website: avahoffman.com
About Us
Marta Karas
PhD Candidate, Department of Biostatistics, JHSPH
MS in Mathematics
Email: mkaras2@jhmi.edu
Website: martakarass.github.io
About Us - TAs
Grant Schumock
PhD Candidate, Department of Biostatistics, JHSPH
BS in Nuclear Engineering
Email: gschumo1@jhmi.edu
About Us - TAs
Michael Breshock
MSE Student in Biomedical Engineering, Whiting School of Engineering, JHU
BS in Bioengineering
Email: mbresho1@jhu.edu
Website: https://mbreshock.github.io/
What is R?
R is a language and environment for statistical computing and graphics
R is the open source implementation of the S language, which was developed by Bell laboratories in the 70s.
The aim of the S language, as expressed by John Chambers, is “to turn ideas into software, quickly and faithfully”
(source: http://www.r-project.org/, https://en.wikipedia.org/wiki/S_(programming_language), https://en.wikipedia.org/wiki/Bell_Labs)
What is R?
In 1991 Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand began developing R
R is named partly after the first names of the first two authors and a play on the name of S.
R is both open source and open development
(source: http://www.r-project.org/, https://en.wikipedia.org/wiki/R_(programming_language) )
Why R?
High level language designed for statistical computing
Powerful and flexible - especially for data wrangling and visualization
Free (open source)
Extensive add-on software (packages)
Strong community
(source: https://rladies-baltimore.github.io/)
Why not R?
Fairly steep learning curve
“Programming” oriented
Minimal interface
Little centralized support, relies on online community and package developers
Annoying to update
Slower, and more memory intensive, than the more traditional programming languages (C, Java, Perl, Python)
Introductions
What do you hope to get out of the class?
Why do you want to use R?
Photo by Nick Fewings on Unsplash
Course Website
http://jhudatascience.org/intro_to_r
Materials will be uploaded the night before class
Learning Objectives
- Reading data into R
- Recoding and manipulating data
- Writing R functions and using add-on packages
- Making exploratory plots
- Understanding basic programming syntax
- Performing basic statistical tests
Installing R
Install the latest version from: http://cran.r-project.org/
Collection of R packages
We have an R package called jhur that will make sure all the packages are installed.
You can just copy and paste the below code into your console - we’ll explain what it all means in the next day or two
install.packages("remotes")
remotes::install_github("muschellij2/jhur")
Note it may take ~5-10 minutes to run.
Useful (+Free) Resources
- R for Data Science: http://r4ds.had.co.nz/
- Various “Cheat Sheets”: https://www.rstudio.com/resources/cheatsheets/
- Dataquest: https://www.dataquest.io/
- R reference card: http://cran.r-project.org/doc/contrib/Short-refcard.pdf
- UCLA Institute for Digital Research and Education: http://www.ats.ucla.edu/stat/r/
- Quick R: http://statmethods.net/
- Open Case Studies: https://www.opencasestudies.org/
- Tidyverse Skills for Data Science Book: https://jhudatascience.org/tidyversecourse/
- Tidyverse Skills for Data Science Course: https://www.coursera.org/specializations/tidyverse-data-science-r
- Roger Peng or Jeff Leek Courses on Coursera: https://www.coursera.org/
- Course Website: http://jhudatascience.org/intro_to_r/index.html